How to Select a Good Training-data Subset for Transcription: Submodular Active Selection for Sequences

Abstract

Given a large un-transcribed corpus of speech utterances, we address the problem of how to select a good subset for word-level transcription under a given fixed transcription budget. We employ submodular active selection on a Fisher-kernel based graph over un-transcribed utterances. The selection is theoretically guaranteed to be near-optimal. Moreover, our approach is able to bootstrap without requiring any initial transcribed data, whereas traditional approaches rely heavily on the quality of an initial model trained on some labeled data. Our experiments on phone recognition show that our approach outperforms both average-case random selection and uncertainty sampling significantly.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA518795

Entities

People

  • Hui Lin
  • Jeff Bilmes

Organizations

  • University of Washington

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automated Speech Recognition
  • Base Lines
  • Data Sets
  • Electrical Engineering
  • Generative Models
  • Hidden Markov Models
  • Kernel Functions
  • Language
  • Machine Learning
  • Markov Models
  • Models
  • Probability
  • Sampling
  • Sequences
  • Statistical Sampling
  • Training

Fields of Study

  • Computer science

Readers

  • Operations Research
  • Regression Analysis.
  • Speech Processing/Speech Recognition.